The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

References in zbMATH (referenced in 88 articles )

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  1. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  2. Kocsis, Zoltan A.; Swan, Jerry: Genetic programming $+$ proof search $=$ automatic improvement (2018)
  3. Lau, F. Din-Houn; Adams, Niall M.; Girolami, Mark A.; Butler, Liam J.; Elshafie, Mohammed Z. E. B.: The role of statistics in data-centric engineering (2018)
  4. Mohammed, Assem H.; Gadallah, Ahmed M.; Hefny, Hesham A.; Hazman, M.: Fuzzy based approach for discovering crops plantation knowledge from huge agro-climatic data respecting climate changes (2018)
  5. Neven, Frank; Schweikardt, Nicole; Servais, Frederic; Tan, Tony: Finite-state map-reduce computation and relational algebra queries (2018)
  6. Nghiem, Peter P.: Best trade-off point method for efficient resource provisioning in spark (2018)
  7. Pelucchi, Mauro; Psaila, Giuseppe; Toccu, Maurizio: Hadoop vs. Spark: impact on performance of the Hammer query engine for open data corpora (2018)
  8. Sassi Hidri, Minyar; Zoghlami, Mohamed Ali; Ben Ayed, Rahma: Speeding up the large-scale consensus fuzzy clustering for handling big data (2018)
  9. Silberstein, Natalia; Zeh, Alexander: Anticode-based locally repairable codes with high availability (2018)
  10. Xia, Dawen; Lu, Xiaonan; Li, Huaqing; Wang, Wendong; Li, Yantao; Zhang, Zili: A MapReduce-based parallel frequent pattern growth algorithm for spatiotemporal association analysis of mobile trajectory big data (2018)
  11. Brandt, Jörgen; Reisig, Wolfgang; Leser, Ulf: Computation semantics of the functional scientific workflow language cuneiform (2017)
  12. Fegaras, Leonidas: An algebra for distributed Big Data analytics (2017)
  13. Ferraro Petrillo, Umberto; Guerra, Concettina; Pizzi, Cinzia: A new distributed alignment-free approach to compare whole proteomes (2017)
  14. Fuerst, Carlo; Pacut, Maciej; Schmid, Stefan: Data locality and replica aware virtual cluster embeddings (2017)
  15. Gentle, James E.: Matrix algebra. Theory, computations and applications in statistics (2017)
  16. Gong, Xueqing; Sung, Chi Wan: Zigzag decodable codes: linear-time erasure codes with applications to data storage (2017)
  17. Kanavos, Andreas; Nodarakis, Nikolaos; Sioutas, Spyros; Tsakalidis, Athanasios; Tsolis, Dimitrios; Tzimas, Giannis: Large scale implementations for Twitter sentiment classification (2017)
  18. Lanza, Daniel; Chávez, F.; Fernandez, Francisco; Garcia-Valdez, M.; Trujillo, Leonardo; Olague, Gustavo: Profiting from several recommendation algorithms using a scalable approach (2017)
  19. Lefticaru, Raluca; Macías-Ramos, Luis F.; Niculescu, Ionuţ Mihai; Mierlă, Laurenţiu: Agent-based simulation of kernel P systems with division rules using FLAME (2017)
  20. Luo, Taibo; Zhu, Yuqing; Wu, Weili; Xu, Yinfeng; Du, Ding-Zhu: Online makespan minimization in MapReduce-like systems with complex reduce tasks (2017)

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